Triple

T1405646
Position Surface form Disambiguated ID Type / Status
Subject Lucerne E31684 entity
Predicate hasTwinTown P919 FINISHED
Object Potsdam E13693 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Potsdam | Statement: [Lucerne, hasTwinTown, Potsdam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Potsdam
Context triple: [Lucerne, hasTwinTown, Potsdam]
  • A. Potsdam chosen
    Potsdam is a historic German city near Berlin, known for its palaces, parks, and role in major 20th-century diplomatic events.
  • B. Neustrelitz
    Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
  • C. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • D. Dresden
    Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
  • E. Weimar
    Weimar is a historic German city renowned as a center of culture and the arts, associated with figures like Goethe and Schiller and pivotal movements in modern design and architecture.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c3bc55a08190a4dfe13a5378aff3 completed March 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69adc986352881909dcc435095bc0f8f completed March 8, 2026, 7:09 p.m.
Created at: March 1, 2026, 7:59 p.m.